Zwitterion-Modified PMMA Interlayers for Reliable Dual-Mode Organic Neuromorphic Devices
Organic electrochemical transistors (OECTs) and electrolyte-gated organic field-effect transistors (EGOFETs) represent promising technologies for neuromorphic computing. Yet, conventional dual-mode devices suffer from fundamental performance trade-offs, where optimization for one mode compromises the other. The primary challenge stems from incompatible interfacial requirements: EGOFETs require stable polarization layers preventing ion penetration, while OECTs demand efficient ionic transport for volumetric doping. Here, we present a novel materials engineering strategy employing zwitterionic-modified poly(methyl methacrylate) (PMMA-ZI) as an interlayer to address this fundamental incompatibility. The amphiphilic zwitterionic moieties simultaneously enhance dipolar polarization for EGOFET operation and facilitate balanced ion transport for OECT functionality through controlled electrostatic interactions. PMMA-ZI devices demonstrate remarkable performance enhancements, with a 13.57-fold improvement in the volumetric capacitance-mobility product to 57.25 F cm<sup>-1</sup> V<sup>-1</sup> s<sup>-1</sup> in OECT mode, compared to conventional PMMA interlayers. The devices exhibit exceptional synaptic plasticity with 6.12 times improved memory retention and successful implementation of 4-bit reservoir computing for pattern recognition. This work establishes a new paradigm for dual-mode organic transistors, enabling uncompromised multifunctional operation essential for next-generation neuromorphic computing and bioelectronics applications.
Authors
- Yu Kyeong Kim (ORCID: https://orcid.org/0000-0002-3282-822X)
- Do‐Hoon Hwang
- Yonghee Kim
- Seong Bin Woo
- Eun Kwang Lee
- Soo Hyun Lee
Institutions
- Pusan National University (KR)
- Pukyong National University (KR)
Publication Details
- Journal
- ACS Applied Materials & Interfaces
- Published
- 2026-03-23
- DOI
- https://doi.org/10.1021/acsami.6c02631
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00